A New Multi-objective Job Shop Scheduling with Setup Times Using a Hybrid Genetic Algorithm

Fakhrzad, Mohammad Bagher; Sadeghieh, Ahmad; Emami, Leila

doi:10.5829/idosi.ije.2013.26.02b.11

Cited by 6 publications

(2 citation statements)

References 41 publications

(45 reference statements)

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“…And according to Zitzler (1999), Spacing assess the standard deviation of the distances among solutions of the pareto front and the NOS metric (Number of found solutions), compare the number of the pareto solutions in pareto optimal front. In this paper, we have used four criteria which are among the most used metrics in the area of multi-objective optimization specially in scheduling problems, to measure the solutions' quality for the proposed algorithms: (Geramianfar, Pakzad, Golhashem, and Tavakkoli-Moghaddam, 2013), (Fakhrzad et al, 2012) and (Arjmand & Najafi, 2015).…”

Section: Evaluation Methodsmentioning

confidence: 99%

A hybrid algorithm for unrelated parallel machines scheduling

Nikabadi¹,

Naderi²

2016

10.5267/j.ijiec

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In this paper, a new hybrid algorithm based on multi-objective genetic algorithm (MOGA) using simulated annealing (SA) is proposed for scheduling unrelated parallel machines with sequencedependent setup times, varying due dates, ready times and precedence relations among jobs. Our objective is to minimize makespan (Maximum completion time of all machines), number of tardy jobs, total tardiness and total earliness at the same time which can be more advantageous in real environment than considering each of objectives separately. For obtaining an optimal solution, hybrid algorithm based on MOGA and SA has been proposed in order to gain both good global and local search abilities. Simulation results and four well-known multi-objective performance metrics, indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA) and SA in terms of each objective and significantly in minimizing the total cost of the weighted function.

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Section: Evaluation Methodsmentioning

confidence: 99%

A hybrid algorithm for unrelated parallel machines scheduling

Nikabadi¹,

Naderi²

2016

10.5267/j.ijiec

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“…Thus identifying all the complexity drivers and their interrelations that lead to unpredictable outcomes in supply chain is the first step in managing the complexity. Firms within supply chain are interested to address the dominant drivers rather than addressing all the drivers [9].…”

Section: Supply Chain and Its Influential And Influenced Factorsmentioning

confidence: 99%

A fuzzy DEMATEL approach for evaluating the risk factors

2021

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Many risk factors are dependent on each other and taking this into account can help manage appropriate decisions. The poor evaluation of these factors will impose high costs in many real applications, especially for a supply chain. There are different methods for risk evaluating and their effects for ranking them. For instant, in the fuzzy DEMATEL method, the experts’ linguistic opinions and preferences on the agent effects are used as the input. The important point is how these opinions are aggregated to produce the less computational error. In this regard, this paper proposes a new method based on statistical inferences for a fuzzy DEMATEL approach for evaluating the factors of a supply chain. This method was applied to a case study and the results showed that the proposed method was better than other methods for integrating the experts’ opinions in a supply chain. However, this method was proposed for the evaluation of supply chain factors, it can be applied to other systems as well.

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